Control and Spread of Contagion in Networks

John Higgins and Tarun Sabarwal

Abstract: We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, and computationally tractable algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations. Our analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework.

Date: 2023-07
New Economics Papers: this item is included in nep-gth, nep-net and nep-ure
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Control and Spread of Contagion in Networks (2022)
Working Paper: Control and Spread of Contagion in Networks (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2308.00062

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